Text Understanding with the Attention Sum Reader Network
关键词 Bi-GRU, Bi-LSTM, attention sum 来源 arXiv 2016.03.04 (published at ACL 2016) 问题 使用带attention的深度模型解决完型填空问题 技术细节 模型比attentive reader简单，分以下几步： 使用双向GRU/LSTM单元计算docment每个词的拼接词向量doc_endcoer 使用双向GRU/LSTM单元
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相关文章
- 1. Text Understanding with the Attention Sum Reader Network
- 2. 《 Text Understanding with the Attention Sum Reader Network 》——Attention Sum Reader
- 3. 论文《Text Understanding with the Attention Sum Reader Network》笔记
- 4. 【论文笔记02】Text Understanding with the Attention Sum Reader Network
- 5. 【论文笔】AS Reader：Text Understanding with the Attention Sum Reader Network
- 6. Understanding Safari Reader
- 7. Understanding SIP with Network Address Translation (NAT)
- 8. Translation with a Sequence to Sequence Network and Attention
- 9. text to image（一）:《GENERATING IMAGES FROM CAPTIONS WITH ATTENTION》
- 10. SaGAN:Generative Adversarial Network with Spatial Attention for Face Attribute Editing